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Paddy Plant Disease Recognition, Risk Analysis, and Classification Using Deep Convolution Neuro-Fuzzy Network

Journal of Mobile Multimedia 2022年第2期18卷 325-348页

作者： Vinoth Kumar, V. Karthick Raghunath, K.M. Rajesh, N. Venkatesan, Muthukumaran Joseph, Rose Bindu Thillaiarasu, N. School of Computer Science and Engineering Jain (Deemed to be University) Bangalore India Department of Computer Science and Engineering MVJ College of Engineering Bangalore India University of Technology and Applied Science Shinas Oman Department of mathematics School of Applied Sciences REVA University Karnataka Bengaluru India Department of Mathematics Christ Academy Institute for Advanced Studies Bangalore India School of Computing and Information Technology REVA University Bengaluru India

A significant number of the world’s population is dependent on rice for survival. In addition to sugarcane and corn, rice is said to be the third most growing staple food in the world. As a consequence of intensive usage of man-made fertilizers, paddy plant diseases have also risen at a faster pace in current history. Exploring the possible disease spread and classifying to detect the consequent impact at an early stage will prevent the loss and improve rice production. The core task of this research is to recognize and quantify different kinds of infections (disease) affecting the paddy plant crop, such as brown spots, bacterial blight, and leaf blasts. Both detection and recognition are carried out based on the risk analysis of paddy crop leaf images. We suggest a Deep Convolutional Neuro-Fuzzy Method (DCNFM) that combines one of the advanced machine learning variant, namely deep convolutional neural networks (DCNNs) and uncertainty handler called fuzzy logic. The synthesis has the benefits of both fuzzy logic and DCNNs when dealing with unstructured data, extracting essential features from imprecise and ambiguous datasets. From the crop field, continuous image data are captured through image sensors and fed as a primary input to the proposed model to analyze the risk and then later to classify them for precise recognition/detection of the disease. The detection/recognition rate of the DCNFM is found to be 98.17% which is comparatively found to be effective in comparison with the traditional CNN model. © 2021 River Publishers

关键词： Accuracy Blast Bronzing Convolution neuro-fuzzy Crown rot Disease classification Head blight Paddy

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DeepSight: Enhancing Deepfake Image Detection and Classification through Ensemble and Deep Learning Techniques 5

DeepSight: Enhancing Deepfake Image Detection and Classifica...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Manju, T. Kalarani, S. St. Joseph's Institute of Technology Information Technology Tamil Nadu Chennai India PSG Institute of Technology and Applied Research Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350367171

in today's digital age, deepfake images poses a significant threat to multimedia content authenticity and integrity. Detecting and classifying deepfake images with high accuracy is crucial to addressing this growing challenge. Deepfake images, generated using advanced machine learning techniques, have become a significant concern due to their potential to deceive individuals and manipulate information. This paper presents DeepSight, an innovative approach that combines ensemble learning techniques with deep learning models to enhance deepfake image detection and classification. DeepSight leverages the diversity of multiple classifiers trained on distinct feature representations extracted from the input image data. The framework begins with an initial assessment to determine whether the image has been manipulated. Subsequently, the image goes through deep feature extraction using Convolutional Neural Networks (CNNs). The resulting feature vectors are then classified using Random Forest, KNearest Neighbors, and XGBoost, with hyper-parameter optimization. Experimental evaluations on benchmark datasets demonstrate DeepSight's superior performance compared to state-of-the-art methods, achieving higher accuracy and robustness across various deepfake generation techniques and quality levels. Notably, DeepSight achieves the highest accuracy of 97.5% when utilizing DenseNet and XGBoost. Overall, DeepSight represents a significant advancement in deepfake image detection and classification. It provides a reliable solution to address deceptive visual content in digital environments. © 2024 IEEE.

关键词： Contrastive Learning

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Data Security and Privacy in the Smart Healthcare System 1

Data Security and Privacy in the Smart Healthcare System

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1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Raje, V.V. Mohite, R.V. Srivastava, Shiplee Lipi, Kumari Gavhane, Yogesh Devi, T. Jayasri Department of Community Medicine Krishna Karad India Chandigarh University Department of Mathematics Punjab India Akhildev Ipr and Research Services Department of Intellectual Property Rights Delhi India Government College of Pharmacy Department of Pharmaceutics Karad India Institute of Aeronautical Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350360707

AI and the Internet of Things (IoT) have lately generated previously imagined prospects for enhancing clinical and patient services, lowering costs, and increasing community health. At the same time, each of these instruments introduces new security and privacy concerns that most translational investigators are unprepared to address, while claiming ultimate responsibility over them. In this sense, how to secure data security as well as user privacy while collecting data from medical mobile devices is an issue worth investigating. This article investigates information gathered by healthcare wearable devices from technological, administration, and legal perspectives, and investigations data safety and privacy issues for medical treatment wearable device in order to safeguard data safeguarding and user privacy while also promoting the long-term developments for both the healthcare wearables technology industry and scientific data collected. © 2024 IEEE.

关键词： Differential privacy

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Image Generation of Tomato Leaf Disease Identification Based on Small-ACGAN

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computers, Materials & Continua 2023年第7期76卷 175-194页

作者： Huaxin Zhou Ziying Fang Yilin Wang Mengjun Tong School of Mathematics and Computer Science Zhejiang A&F UniversityHangzhou311300China Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research Hangzhou311300China School of Humanities and Law Zhejiang A&F UniversityHangzhou311300China Department of Computer Science and Engineering Lehigh UniversityPA18015USA

Plant diseases have become a challenging threat in the agricultural *** learning approaches for plant disease detection and classification have been adopted to detect and diagnose these diseases ***,deep learning entails extensive data for training,and it may be challenging to collect plant *** though plant datasets can be collected,they may be uneven in *** a result,the problem of classification model overfitting *** study targets this issue and proposes an auxiliary classifier GAN(small-ACGAN)model based on a small number of datasets to extend the available ***,after comparing various attention mechanisms,this paper chose to add the lightweight Coordinate Attention(CA)to the generator module of Auxiliary Classifier GANs(ACGAN)to improve the image ***,a gradient penalty mechanism was added to the loss function to improve the training stability of the *** show that the proposed method can best improve the recognition accuracy of the classifier with the doubled *** AlexNet,the accuracy was increased by 11.2%.In addition,small-ACGAN outperformed the other three GANs used in the ***,the experimental accuracy,precision,recall,and F1 scores of the five convolutional neural network(CNN)classifiers on the enhanced dataset improved by an average of 3.74%,3.48%,3.74%,and 3.80%compared to the original ***,the accuracy of MobileNetV3 reached 97.9%,which fully demonstrated the feasibility of this *** general experimental results indicate that the method proposed in this paper provides a new dataset expansion method for effectively improving the identification accuracy and can play an essential role in expanding the dataset of the sparse number of plant diseases.

关键词： Deep learning ACGAN CA gradient penalty tomato diseases identification

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Italian-Chinese Neural Machine Translation: results and lessons learnt 23

Italian-Chinese Neural Machine Translation: results and less...

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3rd ACM Conference on Information Technology for Social Good, GoodIT 2023

作者： Delnevo, Giovanni Im, Marcus Tse, Rita Lam, Chan-Tong Tang, Su-Kit Salomoni, Paola Pau, Giovanni Ghini, Vittorio Mirri, Silvia Department of Computer Science and Engineering University of Bologna Cesena Italy Faculty of Applied Sciences Macao Polytechnic University China Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence Ministry of Education Macao Polytechnic University China

ISBN: (纸本)9798400701160

Today, access to the Internet provides access to various forms of knowledge like free online lecture series offered by prestigious universities, massive open online courses, films and books, and Wikipedia. In addition, it is possible to join online communities on any topic of interest, get to know people with common interests, exchange thoughts and participate in debates. To enable access to these unprecedented knowledge bases, it is crucial to be able to translate texts into any language known by users. For this reason, Machine Translation has been a very active research field for the last thirty years. In this paper, we investigate the task of Chinese-Italian translations by exploiting Neural Machine Translation approaches. We trained several deep neural networks starting from two already available datasets containing Chinese-Italian parallel corpora. Then, we compared their performance against some of the most common machine translation services freely available online. In particular, we take advantage of Microsoft Translator, Google Translate, DeepL, and ModernMT. © 2023 Owner/Author.

关键词： Neural machine translation

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research Challenges and Future Facet of Cellular Computing 2

Research Challenges and Future Facet of Cellular Computing

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Alnawayseh, Saif E. A. Khan, Tahir Abbas Farooq, Umer Zulfiqar, Sidra Khan, Shan Al-Kassem, Amer Hani Mutah Univesity Faculty of Engineering Electrical Engineering Department Jordan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan LGU Department of Computer Science Lahore Pakistan College of Business Management Sharjah United Arab Emirates

ISBN: (纸本)9798350335644

cellular computing is promising and exciting domain. As the field continues to evolve, several future facets are expected to emerge. One potential facet is the development of more sophisticated and efficient cellular-based computing systems that can perform complex calculations and data processing tasks. This could lead to the creation of new tools and technologies in various fields, including medicine, biotechnology, and environmental monitoring. Moreover, technology has changed the way of communication, sharing and exchanging data. Mobile computing has grown to be very vital as the number of cellular devices have elevated fairly in the preceding years. In this paper, we have mentioned cellular computing, aspects of cell computing, working of cell computing. Also, we have discussed that what challenges are most common in the development of cellular computing. In last we discussed merits, demerits, application, and its future. © 2023 IEEE.

关键词： Mobile computing

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Slotted ALOHA with Code Combining for IoT Networks 3

Slotted ALOHA with Code Combining for IoT Networks

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3rd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023

作者： Tyrovolas, Dimitrios Chrysologou, Athanasios Chondrogiannis, Georgios Tegos, Sotiris Mekikis, Prodromos-Vasileios Diamantoulakis, Panagiotis Ioannidis, Sotiris Liaskos, Christos Chatzidiamantis, Nestor Karagiannidis, George Aristotle University of Thessaloniki Dept. of Electrical and Computer Engineering Thessaloniki Greece Technical University of Chania Dept. of Electrical and Computer Engineering Chania Greece University of Ioannina Computer Science Engineering Department Ioannina Greece Cyber Security Systems and Applied Ai Research Center Lebanon

ISBN: (纸本)9798350333732

Internet of Things (IoT) networks are expected to be a key enabler technology for smart cities, due to their ability to offer real-time monitoring. In this work, we propose a novel medium access control (MAC) protocol, inspired by the synergy of slotted ALOHA and hybrid automatic repeat request with code combining (HARQ-CC), termed as code combining slotted ALOHA (CCSA), to improve the performance of IoT networks with multiple randomly deployed sensors. Specifically, we provide analytical closed-form expressions for the network's outage probability as well as for its average throughput when CCSA is applied. Finally, we provide simulation results that reveal the authenticity of the derived expressions and reveal the capabilities of HARQ-CC to significantly improve the network's performance when the sensors' access is based on a random access scheme. © 2023 IEEE.

关键词： Wireless sensor networks

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UML and NFR-framework based method for the analysis of the requirements of an information system

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International Journal of Information Technology (Singapore) 2023年第1期15卷 411-422页

作者： Arif, Mohd. Mohammad, Chaudhary Wali Sadiq, Mohd. Computer Science and Technology Research Group Department of Applied Sciences and Humanities Faculty of Engineering and Technology Jamia Millia Islamia A Central University New Delhi 110025 India Software Engineering Lab Computer Engineering Section UPFET Jamia Millia Islamia A Central University New Delhi 110025 India

Software requirements are the expectation of stakeholders which are identified and modeled by various requirements elicitation and modeling techniques like traditional methods, goal oriented methods, and unified modeling language (UML), etc. The identified software requirements are broadly classified into functional requirements (FRs) and non-functional requirements (NFRs). Based on our literature review, we found that most of the focus in the area of software engineering is on the analysis of FRs as compared to NFRs. For the successful development of any information system both FRs and NFRs should be considered equally during the elicitation and modeling process;and ignoring NFRs may lead to failure of software. Therefore, to address this issue, we developed a method for the analysis of both FRs and NFRs so that a successful software product can be developed by a software company. In the proposed method, FRs are modeled by UML use-case diagram, class-diagram, and activity diagram. The NFRs are analyzed using NFR-framework;and NFR propagation rules have been derived from the requirements of an information system. In this framework, fuzzy based approach has been used to analyze the contribution links of softgoal interdependency graph. The applicability of the proposed method is discussed by using FRs and NFRs of library information system. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Functional requirements Library information system Non-functional requirements Software requirements UML

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Preventing Diabetes: Substituting Processed Foods and Nutritional Chatbot Assistance 1

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5th International Conference on applied Technologies, ICAT 2023

作者： Solano, Pablo Herrera, Víctor Abril-Ulloa, Victoria Espinoza-Mejía, Mauricio Faculty of Engineering University of Cuenca Cuenca Ecuador Technologies Applied to Health Research Group Faculty of Medical Sciences University of Cuenca Cuenca Ecuador Computer Science Department University of Cuenca Cuenca Ecuador

ISBN: (数字)9783031589539

ISBN: (纸本)9783031589522

Type 2 Diabetes Mellitus (T2DM) is one of the biggest threats to Ecuador’s health. The intake of processed foods has been linked to a higher risk of T2DM. This paper proposes FoodSub, a mobile application to recommend substitutes for processed foods using the NOVA Classification. Nutrient-based food clustering is used to identify substitute pairs between processed and unprocessed foods. The recommendations are supported and personalized using a knowledge graph that contains foods, dietary guidelines, and user information. In addition, a chatbot is implemented to answer simple questions about foods. This chatbot is developed using a Large Language Model (LLM) to query the knowledge graph. The mobile application and the chatbot are evaluated in terms of usability;both perform well, but there is room for improvement. Additionally, the recommendations’ performance is evaluated through expert verification. The recommendations perform well when issues like food transformation processes, flavor, context, or meal time are not relevant. Future work will consider the enhancement of the chatbot and the improvement of substitute recommendations for the relevant cases. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Nutrition

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Be Everywhere - Hear Everything (BEE): Audio Scene Reconstruction by Sparse Audio-Visual Samples

Be Everywhere - Hear Everything (BEE): Audio Scene Reconstru...

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International Conference on computer Vision (ICCV)

作者： Mingfei Chen Kun Su Eli Shlizerman Department of Electrical & Computer Engineering University of Washington Department of Applied Mathematics University of Washington

Fully immersive and interactive audio-visual scenes are dynamic such that the listeners and the sound emitters move and interact with each other. Reconstruction of an immersive sound experience, as it happens in the scene, requires detailed reconstruction of the audio perceived by the listener at an arbitrary location. The audio at the listener location is a complex outcome of sound propagation through the scene geometry and interacting with surfaces and also the locations of the emitters and the sounds they emit. Due to these aspects, detailed audio reconstruction requires extensive sampling of audio at any potential listener location. This is usually difficult to implement in realistic real-time dynamic scenes. In this work, we propose to circumvent the need for extensive sensors by leveraging audio and visual samples from only a handful of A/V receivers placed in the scene. In particular, we introduce a novel method and end-to-end integrated rendering pipeline which allows the listener to be everywhere and hear everything (BEE) in a dynamic scene in real-time. BEE reconstructs the audio with two main modules, Joint Audio-Visual Representation, and Integrated Rendering Head. The first module extracts the informative audio-visual features of the scene from sparse A/V reference samples, while the second module integrates the audio samples with learned time-frequency transformations to obtain the target sound. Our experiments indicate that BEE outperforms existing methods by a large margin in terms of quality of sound reconstruction, can generalize to scenes not seen in training and runs in real-time speed.

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